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| Hauptseite > Publikationsdatenbank > A holistic approach for multi-spectral Sentinel-2 super-resolution and spectral evaluation > print |
| Hauptseite > Publikationsdatenbank > A holistic approach for multi-spectral Sentinel-2 super-resolution and spectral evaluation > print |
| 001 | 1047697 | ||
| 005 | 20260107202516.0 | ||
| 024 | 7 | _ | |a 10.1080/01431161.2025.2549132 |2 doi |
| 024 | 7 | _ | |a 0143-1161 |2 ISSN |
| 024 | 7 | _ | |a 1366-5901 |2 ISSN |
| 024 | 7 | _ | |a 10.34734/FZJ-2025-04463 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2025-04463 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 620 |
| 100 | 1 | _ | |a Major, David |0 0000-0002-9091-3684 |b 0 |e Corresponding author |
| 245 | _ | _ | |a A holistic approach for multi-spectral Sentinel-2 super-resolution and spectral evaluation |
| 260 | _ | _ | |a London |c 2025 |b Taylor & Francis |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a Images provided by the European Copernicus Sentinel-2 satellites are valuable and easily accessible sources of remote sensing data for tasks across various fields. These data have a high spectral and temporal resolution, but a rather low spatial resolution, limiting their applicability for many tasks. In agricultural tasks, such as crop monitoring of small land parcels, the use of these data for fine-scale analysis is contingent upon the enhancement of spatial resolution while maintaining spectral fidelity. In this work, we propose a comprehensive single-image super-resolution reconstruction workflow that ensures both properties and is divided into two parts. First, a deep learning-based super-resolution reconstruction approach is applied to improve the spatial resolution of multi-spectral Sentinel-2 images to 2.5 m. For this purpose, a novel method is applied to achieve super-resolution of multiple spectral bands where associated real-word reference data is only partially available. It learns to increase the spatial resolution while preserving spectral accuracy of 10 m bands using high-resolution data from an auxiliary satellite with spectral correspondence, and 20 m bands without reference data using synthetic Sentinel-2 pairs. Second, the suitability of the method to subsequent agricultural tasks is evaluated by measuring the discrepancy between the super-resolved and reference data through a novel spectral knowledge-based validation method. This method leverages mappings of reflectances to spectral categories that enable assessing the spectral fidelity of super-resolved outputs, which is complementary to existing image quality assessment metrics, but with greater depth. The promising spectral validation results suggest that our super-resolution reconstruction pipeline has a great potential for agricultural applications. |
| 536 | _ | _ | |a 2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217) |0 G:(DE-HGF)POF4-2173 |c POF4-217 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Horváth, Zsolt |0 0000-0001-7188-6564 |b 1 |
| 700 | 1 | _ | |a Kröber, Felix |0 P:(DE-Juel1)206952 |b 2 |u fzj |
| 700 | 1 | _ | |a Augustin, Hannah |0 0000-0002-3334-5350 |b 3 |
| 700 | 1 | _ | |a Sudmanns, Martin |0 0000-0002-0473-1260 |b 4 |
| 700 | 1 | _ | |a Ševčík, Petr |0 0009-0005-1564-8525 |b 5 |
| 700 | 1 | _ | |a Baraldi, Andrea |0 0000-0001-5196-9944 |b 6 |
| 700 | 1 | _ | |a Berg, Astrid |0 0000-0002-2300-2661 |b 7 |
| 700 | 1 | _ | |a Cornel, Daniel |0 0000-0002-2481-6720 |b 8 |
| 700 | 1 | _ | |a Tiede, Dirk |0 0000-0002-5473-3344 |b 9 |
| 773 | _ | _ | |a 10.1080/01431161.2025.2549132 |g Vol. 46, no. 20, p. 7437 - 7464 |0 PERI:(DE-600)1497529-4 |n 20 |p 7437 - 7464 |t International journal of remote sensing |v 46 |y 2025 |x 0143-1161 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1047697/files/A%20holistic%20approach%20for%20multi-spectral%20Sentinel-2%20super-resolution%20and%20spectral%20evaluation.pdf |y OpenAccess |
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